Kwang-Ok Park; Jong Kyung Kim; Se Young Kim; Sunju Chang

doi:10.4040/jkan.2012.42.1.9

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Original Article
A Model on Turnover Intention of Chief Nurse Officers: Kwang-Ok Park, Jong Kyung Kim, Se Young Kim, Sunju Chang; Journal of Korean Academy of Nursing 2012;42(1):9-18.
DOI: https://doi.org/10.4040/jkan.2012.42.1.9
Published online: February 29, 2012

¹Assistant Professor, Department of Nursing, Sunchon National University, College of Life Science and Natural Resources, Sunchon, Korea.

²Assistant Professor, Department of Nursing, Dankook University, Cheonan, Korea.

³Full-time lecturer, College of Nursing, Eulji University, Daejeon, Korea.

⁴Critical Care Nurse Educator, Seoul National University Hospital, Seoul, Korea.

Address reprint requests to: Kim, Jong Kyung. Department of Nursing, Dankook University, San#29, Anseo-dong, Dongnam-gu, Cheonan 330-714, Korea. Tel: +82-41-550-3893, Fax: +82-41-559-7902, jongkimk@dankook.ac.kr

• Received: April 9, 2011 • Accepted: February 5, 2012

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Abstract
REFERENCES

Abstract

Purpose
The purpose of this study was to test the turnover intention model for chief nurse officers in general hospitals. The variables for the study included job stress, social support, job satisfaction, and organization commitment.
Methods
A predictive, non-experimental design was used with a sample of 144 chief nurse officers from 144 general hospitals. Data were collected using self-administered questionnaires and analyzed using SPSS, AMOS program.
Results
The overall fitness of the hypothetical model to the data was good (χ²=16.80, p=.052, GFI=.96, AGFI=.90, NFI=.97, CFI=.99). Job stress, social support, job satisfaction, and organization commitment explained 59.0% of the variance in turnover intention by chief nurse officers. Both organization commitment and social support directly influenced turnover intention for chief nurse officers, and job stress and job satisfaction indirectly influenced turnover intention.
Conclusion
The results imply that chief nurse officers in hospitals need social support and management of job stress to increase job satisfaction and organization commitment, and lower turnover intention.
Keywords: Personnel turnover; Job satisfaction; Stress; Physiological; Social support; Nurse administrators

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Figure 1

Theoretical framework of study.

Figure 2

Path diagram of the model.

x1=Personal occupational stress; x2=Organizational occupational stress;

x3=Peer support; x4=Family support; y1=Job satisfaction; y2=Organizational commitment;

y3=Turnover intention.

Table 1

General Characteristics of Participants (N=144)

CNO=Chief nurse officer.

Table 2

Descriptive Statistics of Observed Variables (N=144)

Table 3

Correlations among Observed Variables

^*p<.05; ^**p<.01.

Table 4

Effects of Predictive Variables on Endogenous Variables in the Model

SMC=Squared multiple correlations.

^*p<.05; ^**p<.01.

Figure & Data

REFERENCES

Citations

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A Model on Turnover Intention of Chief Nurse Officers

J Korean Acad Nurs. 2012;42(1):9-18. Published online February 29, 2012

DOI: https://doi.org/10.4040/jkan.2012.42.1.9

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A Model on Turnover Intention of Chief Nurse Officers

Figure 1 Theoretical framework of study.

Figure 2 Path diagram of the model. x1=Personal occupational stress; x2=Organizational occupational stress; x3=Peer support; x4=Family support; y1=Job satisfaction; y2=Organizational commitment; y3=Turnover intention.

Figure 1

Figure 2

A Model on Turnover Intention of Chief Nurse Officers

Table 1

General Characteristics of Participants (N=144)

CNO=Chief nurse officer.

Table 2

Descriptive Statistics of Observed Variables (N=144)

Table 3

Correlations among Observed Variables

^*p<.05; ^**p<.01.

Table 4

Effects of Predictive Variables on Endogenous Variables in the Model

SMC=Squared multiple correlations.

^*p<.05; ^**p<.01.

Table 1 General Characteristics of Participants (N=144)

CNO=Chief nurse officer.

Table 2 Descriptive Statistics of Observed Variables (N=144)

Table 3 Correlations among Observed Variables

^*p<.05; ^**p<.01.

Table 4 Effects of Predictive Variables on Endogenous Variables in the Model